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The WSDM Cup 2017 Triple scoring challenge is aimed at calculating andassigning relevance scores for triples from type-like relations. Such scoresare a fundamental ingredient for ranking results in entity search. In thispaper, we propose a method that uses neural embedding techniques to accuratelycalculate an entity score for a triple based on its nearest neighbor. We striveto develop a new latent semantic model with a deep structure that captures thesemantic and syntactic relations between words. Our method has been rankedamong the top performers with accuracy - 0.74, average score difference - 1.74,and average Kendall's Tau - 0.35.

Predicting Relevance Scores for Triples from Type-Like Relations using Neural Embedding - The Cabbage Triple Scorer at WSDM Cup 2017